A Market-Based Framework for Multi-Resource Allocation in Fog Computing

• Published in: IEEE/ACM transactions on networking 2019-06, Vol.27 (3), p.1151-1164
• Main Authors: Nguyen, Duong Tung, Le, Long Bao, Bhargava, Vijay K.
• Format: Article
• Language: English
• Subjects: Algorithms; Cloud computing; Companies; Computational modeling; Economic models; Edge computing; End users; Equilibrium; fog computing; General equilibrium; Germanium; Markets; multi-resource allocation; Performance evaluation; privacy-preserving distributed optimization; Resource allocation; Resource management; Sensors
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2019.2912077

Fog computing is transforming the network edge into an intelligent platform by bringing storage, computing, control, and networking functions closer to end users, things, and sensors. How to allocate multiple resource types (e.g., CPU, memory, bandwidth) of capacity-limited heterogeneous fog nodes to competing services with diverse requirements and preferences in a fair and efficient manner is a challenging task. To this end, we propose a novel market-based resource allocation framework in which the services act as buyers and fog resources act as divisible goods in the market. The proposed framework aims to compute a market equilibrium (ME) solution at which every service obtains its favorite resource bundle under the budget constraint, while the system achieves high resource utilization. This paper extends the general equilibrium literature by considering a practical case of satiated utility functions. In addition, we introduce the notions of non-wastefulness and frugality for equilibrium selection and rigorously demonstrate that all the non-wasteful and frugal ME are the optimal solutions to a convex program. Furthermore, the proposed equilibrium is shown to possess salient fairness properties, including envy-freeness, sharing-incentive, and proportionality. Another major contribution of this paper is to develop a privacy-preserving distributed algorithm, which is of independent interest, for computing an ME while allowing market participants to obfuscate their private information. Finally, extensive performance evaluation is conducted to verify our theoretical analyses.